Building a framework vs. institutionalizing the program

So you have a data governance program. Congratulations! Now you’re probably thinking about how you can measure the state of data governance in your enterprise moving forward. Perhaps you’ve compile a list of questions:

How many layers of escalation are built into the framework?

How many permanent council members versus virtual team members are engaged in the program?

What percentage of time do data stewards spend on data governance activities?

The truth is there are dozens of similar questions that can be asked to assess the state of the state, but the only one that really matters is whether the data governance program is proving its value and driving better business results.

I have seen a lot of organizations that have established data governance with a goal of better quality data for better decision-making in the business. The backend measurement of that objective is where it gets a bit dicey (jeopardizing the long-term operations of the program). Data profiling and data quality monitoring can help to determine whether or not your data is of higher quality, but they can’t help to determine whether or not there is value coming from the effort. Are you governing the right data, or just the data that’s easiest to bring under the program’s influence?

You say your data governance program is up and running. I say, “So what? What is it actually doing for your company?” In order for any enterprise-wide initiative to be deemed a success and to become sustainable for the long haul it must produce measureable, valuable outcomes – repeatedly. While driving better quality of data across the heterogeneous application landscape is A goal of data governance, it should not be THE goal. The end state is to create a systemic improvement in the process of managing data to ensure that your business processes are operating at their optimum performance level given their current state. In other words, remove the variable of bad data from the equation so that business processes can be assessed, engineered and optimized based on the ability to perform straight-through processing with no data-driven anomalies to disrupt the flow.

Have you made this connection between data and business process in your program? Are you able to report the economic or risk-mitigation benefits that your data governance program is producing to your company executives? Can you confidently state that compliance to data policies is transforming your organization into the well-oiled machine it was intended to be?

Given the current state of the economy and the tight controls on operational costs, being able to prove the value of any program is the only way to ensure its survival. Value statements must be grounded in fact. Facts regarding the impact of governance must come from measurement. Measurement of the change in the quality of data over time, but more importantly the measurement of the corresponding improvement in business process performance or reduction of risk over that same time period.

Your CFO is probably not all that interested in the data governance program’s operational aspects. However, he or she is keenly interested in the fact that better management of data has reduced the number of manually processed invoices flowing through the order-to-cash process, allowing your company to reduce the overall time to collect cash from your customers (with fewer resources involved in the process) thus reducing the number of days sales outstanding and reducing overall operational costs. That’s what data governance can do for your company!

So, consider a data policy or set of data policies designed to drive greater consistency and control over the critical data elements that affect the behavior of your core processes such as order-to-cash. Assess the performance of that process and the resulting KPI value for a KPI as indicative of overall business performance such as Days Sales Outstanding or Perfect Order Performance, and report the results alongside the level of compliance to those data policies. It is this tight linkage between data and the business processes that use it that ingrains your data policies (and thus your data governance program) as a foundational underpinning of your institution. No compliance to policies means no control over data across the broad landscape. No compliance to policies means no consistency of business process execution. No compliance to policies means no confidence that decisions that are being made are made based on fact.

By using business-oriented data policies as the rallying point between business objectives and data characteristics, you can promote data governance from a simple “good idea” to a mission-critical component of your business infrastructure. Then, when budget considerations for funding the ongoing operations of the data governance program come up, and the executive team asks, “So, what is the data governance program doing for our company?” You can answer with confidence, backed up by measured facts which clearly identify the program as a critical and core business process in the language of the executive team – their KPIs.

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